Agent Workflow
AI Multi-Agent Study Workbench
A study-agent workbench that decomposes long course material into planned, tool-assisted, page-level learning workflows.
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Problem
Long lecture decks and mixed-format notes are brittle under one-shot prompting. The product problem is not simply generating an answer, but preserving context, deciding when to use tools, and making the reasoning path inspectable.
Workflow
- 01Preprocess documents and split the reading task into layout analysis, local retrieval, page explanation, and quality scoring.
- 02Use a LangGraph Planner / Reasoner structure to build global memory before executing chapter and page tasks.
- 03Route only figures, formulas, and special layouts to a visual model while keeping normal text with the primary LLM.
- 04Trigger repair instructions and retries when citations are missing, important points are skipped, or context breaks.
Evidence
Workflow mechanism
Planner / Reasoner, Tool-Use interfaces, retrieval sources, quality scores, and retry rules are the core evidence, not a single prompt.
Process supervision
Execution traces, tool-call paths, failure reasons, and repair outcomes are recorded for future process-reward learning.
Boundary
- This is a workbench and research-product prototype, not a claimed production learning platform at institutional scale.
- The public page excludes private notes, user data, credentials, and raw long-document content.
- Agentic RL is presented as the follow-up direction based on collected traces, not as a completed trained policy model.
Role Mapping
- Agent product: converts a vague learning task into planned, tool-assisted workflow.
- LLM Eval / Agent workflow: exposes quality gates, retry conditions, and process traces.
- Developer-facing AI product: makes model behavior inspectable through topology, evidence, and scoring surfaces.